SAP Predictive Analysis Transform Your Future with Predictive Insight Charles Gadalla, Solution Management February 2013
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Why predictive now?
3
Changing landscapes and new opportunities
Increased Business Interest
Answer more sophisticated questions
Resolve real-time problems
Exploding data volume Expanding data varieties Invest in data to get value
Increasing Technology Performance
Create efficient business models
Reduce data processing time
Increased Data Value (Big Data)
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Extend your analytics capabilities where you want to be…
Sense & Respond Predict & Act
Raw Data
Cleaned Data
Standard Reports
Ad Hoc Reports &
OLAP
Generic Predictive Analytics
Predictive Modeling
Optimization
What happened?
Why did it happen?
What will happen?
What is the best that could happen?
Com
petit
ive
Adv
anta
ge
Analytics Maturity
The key is unlocking data to move decision making from sense & respond to predict & act
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SAP in the Leader’s quadrant!
We’ve come a long way in 6 months…
…from standing start to LEADER!
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SAP’s Predictive Analytics Strategy
Real-time in-memory predictive and next generation visualization and modeling
Empower the Business
Extend the Business Intelligence competency to Advanced Analytics
Embed Predictive into Apps and BI environments
Lend expertise
In-memory processing No data latencies Big Data ready
In Context
Relevant to your business Within the context of your
Industry and LOB scenario
In-time Actionable Insights
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Where SAP has helped customers with Predictive Analytics in Industry
Retail and Consumer Products
Demand forecasting and insights Market basket insights and KVI Markdown (and price)
optimization Size and zone optimization Inventory Optimization Promotional Assessments Identify geographic trends and
performance
Financial Services
Price optimization Product and portfolio optimization Market segmentation Corporate and credit risk
management Customer retention,
segmentation Cross- and up-selling, customer
lifetime value
Manufacturing & Utilities
Demand simulation for configurable products
Supply chain optimization Load demand modeling and
forecasting Smart Energy Meter analytics Customer service, customer
lifetime value Asset efficiency: spare parts,
outages, inventory, risk
High Tech & Telco
Sales Forecasting, Enablement and category management
Customer experience Buyer classification Demand insights
Fraud, Waste and Abuse discovery Analytics enhancement for billing Crime trends and At Risk analytics Predict community movement within
taxing districts Predict likelihood of disease Identify clinical trial outcomes
Public Sector & Healthcare
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How SAP has helped customers with Predictive Analytics in Industry Retail and Consumer Products
10.5M accounts clusterered and segmented within 90 seconds for greater promotional insights
Sell larger basket sizes by identifying products that drive drag-along sales
Reduction of returned goods by 40% led to yearly savings of ~ $50M
Improve revenue and net margin by accelerating profitable merchandising initiatives
High Tech & Telco
React faster and more appropriately to the causes of customer churn
Improved quality while meeting strict timelines within budget by realigning resources to projects that complement their skill sets
Financial Services
Significant improvements to retention and reduction in attrition
Improved revenue and net margin by accelerating profitable marketing initiatives by targeting under-served customer segment
Fraud detection and risk management
Manufacturing & Utilities
Reduced time, effort and costs to develop new products
Reduction in defective products Manufacturing process
improvements Energy trading and grid demand
planning Major improvements to energy
demand and resource requirements
Improved taxpayer compliance and revenue collection by +10%
Improve revenue collection levels, discourage fraudulent behavior with better prediction and investigation and contribute to the reduction of budget deficits
Significant savings by more quickly and precisely identifying fraud
Reduction in compliance and policy audits
Public Sector & Healthcare
Success stories
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Coinstar
• Inventory Optimization
• Real time Offers
• Servicing
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Mitsui Knowledge Industry Healthcare – Speed Research & Improve Patient Support
Business Challenges Reduce delays and minimize the costs associated with new drug
discovery by optimizing the process for genome analysis Improve and speed decision making for hospitals which conduct
cancer detection based on DNA sequence matching
Technical Implementation Leveraged the combination of SAP HANA, R, and Hadoop to
store, pre-process, compute, and analyze huge amounts of data Provide access to breadth of predictive analytics libraries
Benefits For pharmaceutical companies, provide required new drugs on
time and aid identification of “driver mutation” for new drug targets Able to provide a one stop service including genomic data
analysis of cancer patients to support personalized patient therapeutics
Our solution is to incorporate SAP HANA along with Hadoop and R to create a single real-time big data platform. With this we have found a way to shorten the genome analysis time from several days down to only 20 minutes.
Yukihisa Kato, CTO and Director of MITSUI KNOWLEDGE INDUSTRY
408,000x
faster than traditional disk-based systems in a technical PoC
216x faster by reducing genome analysis from several days to only 20 minutes making real-time cancer/drug screening possible
“ ”
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Bigpoint Gaming Industry - Predictive Game Player Behavior Analysis
Business Challenges Increase conversion rates from free paying player Increase the average revenue per paying player Decrease churn – keep paying players playing longer
Technical Challenges Leverage real-time data processing in SAP HANA and
classification algorithms with R integration for SAP HANA to deliver personalized context-relevant offers to players
Analyze vast amounts of historical and transactional data to forecast player behavior patterns
Benefits Real-time insights Per player profitability analysis and increased understanding of
player behavior Increase data volume and processing capabilities to
communicate personalized messages to players
At Bigpoint in the Battlestar Galactica online game, we have more than 5,000 events in the game per second which we have to load in SAP HANA environment and to work on it to create an individualized game environment to create offers for them. In this co-innovation project with SAP HANA, using Real Time Offer Management Bigpoint, we hope to increase revenue by 10-30%.
Claus Wagner, Senior Vice President SAP Technology, Bigpoint
“ ”
5,000 events per second loaded onto SAP HANA (not possible before)
10-30% increase in revenue per year
Interactive data analysis leading to improved design thinking and game planning
SAP Predictive Analysis Solution Overview
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Visualize, discover, and share hidden insights
• Advanced visualization designed where you’d expect it – natively from within the modelling tool
• Share insights via PMML and with other BI client tools
SAP Predictive Analysis
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SAP Predictive Analytics Solution
SAP Predictive Analytics Solution
RDBMS, IQ, BW, Universes, XLS…
Real Time Execution Environment
develop & score in-memory Powered by HANA
SAP Predictive Analysis
modern design, model, visualize
Applications (Industry & LOB) Customer Analytics, Affinity Insight / Unified Demand Forecast for
Retail, Smart Meter Analytics,
Expertise – P
IO, P
artners
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Open Source statistical programming language Over 3,500 add-on packages; ability to write your own functions Widely used for a variety of statistical methods More algorithms and packages than SAS + SPSS + Statistica
Who is using it? Growing number of data analysts in industry, government,
consulting, and academia Cross-industry use: high-tech, retail, manufacturing, CPG,
financial services , banking, telecom, etc.
Why are they using it? Free, comprehensive, and many learn it at college/university Offers rich library of statistical and graphical packages
R is a software environment for statistical computing and graphics
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Options to use SAP Predictive Analysis
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SAP HANA Predictive Ecosystem
SAP HANA Platform
Data Pre-Processing and Loading SAP Data Services, Information Composer, SLT, DXC, Hadoop
Predictive Analysis Library
(PAL) SAP HANA Studio
SAP and Custom
Applications
R Integration for SAP HANA
Business Intelligence
Clients
R
SAP Predictive Analysis
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Predictive Analytics - Roadmap & Resources
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Predictive Analysis Roadmap – Key Themes
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SAP HANA PAL Roadmap*
*These are planned dates and features only and not firm commitments
• HANA PAL SPS06 late Q2 2013*
2013
SPS05
(export)
(import)
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Appendix
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R Integration for SAP HANA Embedded Scenario Embedding R scripts within the SAP HANA database
execution Enhancements are made to the SAP HANA database
to allow R code (RLANG) to be processed as part of the overall query execution plan
This scenario is suitable when the modeling and consumption environment sits on HANA and the R environment is used for specific statistical functions
Send data and R script 1
2 Run the R scripts 3
Get back the result from R to SAP HANA
CREATE FUNCTION LR( IN input1 SUCC_PREC_TYPE, OUT output0 R_COEF_TYPE) LANGUAGE RLANG AS''' CHANGE_FREQ<-input1$CHANGE_FREQ; SUCC_PREC<-input1$SUCC_PREC; coefs<-coef(glm(SUCC_PREC ~ CHANGE_FREQ, family = poisson )); INTERCEPT<-coefs["(Intercept)"]; CHANGEFREQ<-coefs["CHANGE_FREQ"]; result<-as.data.frame(cbind(INTERCEPT,CHANGEFREQ)) '''; TRUNCATE TABLE r_coef_tab; CALL LR(SUCC_PREC_tab,r_coef_tab ); SELECT * FROM r_coef_tab;
Sample Code in SAP HANA SQLScript
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SAP HANA – Hadoop, Sentiment Analysis Integration
Pre-process data in Hadoop
Load results into HANA
SAP HANA SP5
Hadoop
Log files
Visualize HANA data in SAP Business Objects BI Data Services
4.1
BusinessObjects BI
Visualize HIVE data in SAP Business Objects BI
unstruct
ured data
Text Analysis 31 languages Entity extraction Sentiment analysis
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SAP Predictive Analysis: Algorithms
• Supports In-Process and SAP HANA In-Database Predictive Analytics Algorithms
• In Process Predictive Analysis Algorithms (Desktop) • Data is brought to SBOP PA and analysis is performed in the client • Sources:
• SAP PA Native Algorithms • Open Source ‘R’ integration algorithm
• In Database Predictive Analytics Algorithms (within SAP HANA) • Analysis is done within HANA (no movement of data) and controlled by SBOP PA • Sources:
• SAP HANA Predictive Analysis Library (PAL) algorithms • K-means clustering • Multi-linear regression • KNN (K Nearest Neighbor) • Apriori • C4.5 decision tree
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List of Algorithms in SAP Predictive Analysis 1.0.7
SAP PA Native Algorithms – Outlier Detection Analysis o Inter Quartile Range o Nearest Neighbor Outlier
– Regression Analysis o Exponential Regression o Geometric Regression o Linear Regression o Logarithmic Regression
– Time Series Analysis o Triple Exponential Smoothing
SAP PA with R (out of the box) – Association Analysis o R-Apriori
– Segmentation Analysis o R K-Means
– Decision Tree o R CNR Tree
– Neural Network o R MONMLP o R NNet
– Regression Analysis o R Exponential Regression o R Geometric Regression o R Linear Regression o R Logarithmic Regression o R Multiple Linear Regression
– Time Series Analysis o R Triple Exponential Smoothing o R Single Exponential Smoothing o R Double Exponential Smoothing
SAP PA PAL Algorithms via HANA
• K-means clustering • Multi-linear regression • KNN (K Nearest Neighbor) • Apriori • C4.5 decision tree
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Predictive Analysis & SAP HANA Synergies
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Predictive Analysis
Prediction x Real-time + Big Data = Competitive Advantage
Leverage the complementary capabilities of both SAP Predictive Analysis and SAP HANA
Integrated and optimized for interoperability, enabling the combination of real-time and operational analytics, access to big data, and predictive capabilities
If it’s available through SAP HANA, it can be used for data mining and predictive analysis – gain real-time access to BPC, BW, ERP, Analytic Applications, and more
SAP HANA
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SAP Predictive Analysis Integration with SAP HANA - Today
• Simplified UI/UX for predictive analysis in HANA
• HANA as source of data for In Database Predictive Analysis • HANA table as source • HANA view as source
• Attribute View • Analytical View • Calculation View
• Sample and filter the data in HANA
• Visualize the data in SBOP PA
• HANA as source of data through JDBC • Apply algorithms on the data and perform the analysis • Visualize the results
• Persist the results back to HANA as tables